I don't think so. it is solved by installing openssh-client to the docker image or by adding deploy token to the cloning url in web ui

You can also have the token (token==password) configured as the defauylt user/pass in your agent's clearml.conf
https://github.com/allegroai/clearml-agent/blob/73625bf00fc7b4506554c1df9abd393b49b2a8ed/docs/clearml.conf#L19

Hi FierceHamster54
I'm this is solvable, get in touch with them either in the contact form on the website or email support@clear.ml , should not be complicated to fix 🙂

If we have the time maybe we could PR a fix?!

how to make sure it will traverse only current package?

Just making sure there is no bug in the process, if you call Task.init in your entire repo (serve/train) you end up with "installed packages" section that contains all the required pacakges for both use cases ?

I have separate packages for serving and training in a single repo. I don’t want serving requirements to be installed.

Hmm, it cannot "know" which is which, because it doesn't really trace all the import logs (this w...

How about this one:
https://github.com/allegroai/clearml/blob/master/examples/frameworks/tensorflow/tensorboard_toy.py

Hi OutrageousGrasshopper93
I think that what you are looking for is Task.import_task and Task.export
https://allegro.ai/docs/task.html#trains.task.Task.import_task
https://allegro.ai/docs/task.html#trains.task.Task.export_task

Could you send the full log ?

Okay, let's take a step back and I'll explain how things work.
When running the code (initially) and calling Task.init
A new experiment is created on the server, it automatically stores the git repo link, commit ID, and the local uncommitted changes . these are all stored on the experiment in the server.
Now assume the trains-agent is running on a different machine (which is always the case even if it is actually on the same machine).
The trains-agent will create a new virtual-environmen...

SmarmySeaurchin8 I might be missing something in your description. The way the pipeline works,
the Tasks in the DAG are pre-executed (either with "execute_remotely" or actually fully executed once").
The DAG nodes themselves are executed on the trains-agent , which means they reproduce the code / env for every cloned Task in the DAG (not on the original Tasks).
WDYT?

Can you do it manually, i.e. checkout the same commit id, then take the uncommitted changes (you can copy paste it to diff.txt) then call git apply diff.txt ?

That is exactly that, the trains-agent is replicating the code from the git repo, and trying to apply the git diff (see uncommitted changes section). Obviously it failed 🙂

Hi SmarmySeaurchin8

, I was wondering if I could change the commit id to the current one as well.

Actually that would be possible, but will need a bit of code to support controlling Task properties (not just configuration parameters)

How can I do that without running this Task by it's own?

Assuming you have a committed code that already supports it. You can clone the executed Task, and then change the commit ID to the "latest on branch" (see drop down when editing)

Would t...

sure no prob

OutrageousGrasshopper93
tensorflow-gpu is not needed, it will convert tensorflow to tensorflow-gpu based on the detected cuda version (you can see it in the summary configuration when the experiment sins inside the docker)

How can i set the base python version for the newly created conda env?

You mean inside the docker ?

Okay, I'll make sure we always qoute " , since it seems to work either way.
We will release an RC soon, with this fix.
Sounds good?

Hi OutrageousGrasshopper93
Are you working with venv or docker mode?
Also notice that is you need all gpus you can pass --gpus all

Also what is the docker vserion?

Are you using zsh by any chance?

BTW:

Error response from daemon: cannot set both Count and DeviceIDs on device request.

Googling it points to a docker issue (which makes sense considering):
https://github.com/NVIDIA/nvidia-docker/issues/1026
What is the host OS?

OutrageousGrasshopper93 is "--gpus all" working ?

Hmm, let me check something

Ubuntu? which version?

🙂

All the 3 steps can be found here:
https://github.com/allegroai/trains/tree/master/examples/pipeline

The idea is that it is not necessary, using the trains-agent you can not only launch the experiment on a remote machine, you can override the parameters, not just cmd line arguments, but any dictionary you connected with the Task or configuration...

DilapidatedDucks58 if you have so many parameters, why don't you use the
task.connect_configuration(dict)
It will put it in the artifacts, as an editable json alike string.

You can always access the entire experiment data from python
'Task.get_task(Id).data'
It should all be there.
What's the exact use case you had in mind?

It's dead simple to install:
Pip install trains-agent
the.n you can simply do:
Trains-agent execute --id myexperimentid

Is this the case?

RoundMosquito25 how is that possible ? could it be they are connected to a different server ?